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A Modified Arnoldi Iteration for Transition Probability Matrices of Reversible Markov Chains.

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dc.contributor.author Chong, Joseph M. U.
dc.date.accessioned 2019-05-28T19:56:15Z
dc.date.available 2019-05-28T19:56:15Z
dc.date.issued 2018-08
dc.identifier.uri http://hdl.handle.net/10125/62400
dc.title A Modified Arnoldi Iteration for Transition Probability Matrices of Reversible Markov Chains.
dc.type Thesis
dc.contributor.department Electrical Engineering
dcterms.abstract Reversible Markov chains are used for modeling many physical and network phenomena. The second largest eigenvalue magnitude of the transition probability matrix gives a upper bound on the mixing time of a reversible Markov chain, but is incalculable for large transition probability matrices using typical eigenvalue algorithms. We present the Modified Arnoldi iteration - a modification of the Arnoldi iteration for reversible Markov chains that utilizes sample estimates where matrix operations may be infeasible, thereby being a possible option when usual algorithms are nonviable.
dcterms.description M.S. Thesis. University of Hawaiʻi at Mānoa 2018.
dcterms.language eng
dcterms.publisher University of Hawaiʻi at Mānoa
dcterms.rights All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.
dcterms.type Text
Appears in Collections: M.S. - Electrical Engineering


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